`ggplot`

library(ggplot2)
housing <- read.csv("./dataSets/landdata-states.csv")
head(housing, 5)

##   State region    Date Home.Value Structure.Cost Land.Value
## 1    AK   West 2010.25     224952         160599      64352
## 2    AK   West 2010.50     225511         160252      65259
## 3    AK   West 2009.75     225820         163791      62029
## 4    AK   West 2010.00     224994         161787      63207
## 5    AK   West 2008.00     234590         155400      79190
##   Land.Share..Pct. Home.Price.Index Land.Price.Index Year Qrtr
## 1             28.6            1.481            1.552 2010    1
## 2             28.9            1.484            1.576 2010    2
## 3             27.5            1.486            1.494 2009    3
## 4             28.1            1.481            1.524 2009    4
## 5             33.8            1.544            1.885 2007    4

`ggplot` VS. Basic Plots

is more verbose for simple graphics
is less verbose for complex graphics
only for data.frame
use a different system for adding plot elements

For simple graphic

# With base graph
hist(housing$Home.Value)

# With ggplot
ggplot(housing, aes(x = Home.Value)) +
  geom_histogram()

For complex graphic

# With base graph
plot(Home.Value ~ Date, data = subset(housing, State=='MA'))
points(Home.Value ~ Date, data = subset(housing, State=='TX'), col='red')
legend(1975, 400000, title = 'State',
       c('MA','TX'), col=c('black','red'), pch=c(1,1))

# With ggplot
ggplot(data = subset(housing, State %in% c('MA','TX')),
       aes(x=Date, y=Home.Value, color=State)) +
  geom_point()

Aesthetics and Geometric Objects

A. Aesthetic Mapping: In ggplot, the “aesthetic” means “something you can see”. For example: + position (e.g. on the x and y axis) + color (outside color) + fill (inside color) + shape + linetype + size

B. Geometric Objects: These are the actual marks that we put onto a plot. For example: + points + lines + boxplot… A plot must have at least one geom object, with no upper limit.

help.search('geom_', package='ggplot2')

Scatter/Point Plot

house2001Q1 <- subset(housing, Date==2001.25)
ggplot(house2001Q1, aes(x=Land.Value, y=Structure.Cost)) + # mapping for x and y
  geom_point()

Lines (Prediction Lines)

house2001Q1$pred_sc <- predict(lm(Structure.Cost ~ log(Land.Value), house2001Q1))

ggplot(house2001Q1, aes(x=log(Land.Value), y=Structure.Cost)) +
  geom_point(aes(color=Home.Value)) +
  geom_line(aes(y=pred_sc))

Smoothers

ggplot(house2001Q1, aes(x=log(Land.Value), y=Structure.Cost)) +
  geom_point(aes(color=Home.Value), size=1) +  # !!! size, the fixed aes, is set outside the "aes".
  geom_line(aes(y=pred_sc)) +
  geom_smooth()

Text (Lable points)

ggplot(house2001Q1, aes(x=log(Land.Value), y=Structure.Cost)) +
  geom_point(aes(color=Home.Value, shape=region)) +          # !!! mapping with other fields
  geom_text(aes(label=State), size=2, hjust=0.5, vjust=-0.9) # !!! size & hjust & vjust, the fixed aes, are set outside the "aes".

Exercise I

# Human Development Index (HDI) & Corruption Perception Index (CPI)
dat <- read.csv("./dataSets/EconomistData.csv")
head(dat, 3)

##   X     Country HDI.Rank   HDI CPI            Region
## 1 1 Afghanistan      172 0.398 1.5      Asia Pacific
## 2 2     Albania       70 0.739 3.1 East EU Cemt Asia
## 3 3     Algeria       96 0.698 2.9              MENA

Task 1: Create a scatter plot with CPI on the x axis and HDI on the y axis.
Task 2: Color the points blue.
Task 3: Map the color of the the points to Region.
Task 4: Make the points bigger by setting size to 2
Task 5: Map the size of the points to HDI.Rank

ggplot(dat, aes(x=CPI, y=HDI)) +
  # geom_point(color='blue') +
  # geom_point(aes(color=Region), size=2)
  geom_point(aes(color=Region, size=HDI.Rank))

Statistical Transformations

Statistical Transformations
Some plots, like boxplot and prediction lines, require statistical transformation.

for boxplot, the y values must be transformed to median and 1.5.
for smoother, the y values must be transformed to the predicted values.

Setting Statistical Transformation Arguments
Arguments of stat functions can be passed through geom_ functions. But in order to change it, you have to first determine which stat the geom_ uses, then determine the arguments to that stat.

ggplot(housing, aes(Home.Value)) +
  geom_histogram()

To change the width of each bars in the histogram, we need to state stat_bin first…

ggplot(housing, aes(Home.Value)) +
  geom_histogram(stat='bin', binwidth=4000)

Changing The Statistical Transformation

In geom_bar, the stat is count by default.

housing_agg <- aggregate(Home.Value~State + region, data=housing, FUN = mean)

ggplot(housing_agg, aes(x=State, y=Home.Value)) +
  geom_bar(aes(color=region), stat = 'identity') # specify the 'stat' value

Exercise II

Re-create a scatter plot with CPI on the x axis and HDI on the y axis (as you did in the previous exercise).
Overlay a smoothing line on top of the scatter plot using geom_smooth.
Overlay a smoothing line on top of the scatter plot using geom_smooth, but use a linear model for the predictions. Hint: see ?stat_smooth.
Overlay a smoothing line on top of the scatter plot using geom_line. Hint: change the statistical transformation.
BONUS: Overlay a smoothing line on top of the scatter plot using the default loess method, but make it less smooth. Hint: see ?loess.

ggplot(dat, aes(x=CPI, y=HDI)) +
  geom_point() +
  geom_smooth(method='loess', span=0.3) #  "lm", "glm", "gam", "loess", "rlm"

  # geom_line(stat='smooth', method='loess')

Scales

Controlling Aesthetic Mapping Aesthetic mapping (i.e., with aes()) only says that a variable should be mapped to an aesthetic. It doesn’t say how that should happen.
Describing what colors/shapes/sizes etc. to use is done by modifying the corresponding scale (scale_<aesthetic>_<type>).
The scale in ggplot2 includes:

position
color and fill
size
shape
line type

Common Scale Arguments

ggplot(housing, aes(x=State, y=Home.Price.Index)) +
  theme(legend.position = 'top', axis.text = element_text(size=6)) +
  geom_point(aes(color=Date), 
             alpha=0.5, 
             size=1, 
             position=position_jitter(width=0.15, height=0)) + # adding random noise to a plot to make it easier to read
  scale_x_discrete('State Abbreviation') +
  scale_color_continuous(name='Date', 
                         breaks=c(1976, 1994, 2010), labels=c("'76", "'94", "'10"),
                         low='blue', high='red')

Using different color scales
ggplot2 has a wide variety of color scales.
This is an example of using scale_color_gradient2 to interpolate between three different colors.

ggplot(housing, aes(x=State, y=Home.Price.Index)) +
  theme(legend.position = 'top', axis.text = element_text(size=5)) +
  geom_point(aes(color=Date), alpha=0.5, size=1, 
             position=position_jitter(width = 0.1, height = 0)) +
  
  scale_color_gradient2(name="",
                        breaks=c(1976, 1994, 2013),
                        labels=c("'76","'94","'13"),
                        low='blue', high='red', mid='grey60', midpoint = 1994)

Available Scales Categories:

scale_color_
scale_fill_
scale_size_
scale_shape_
scale_linetype_
scale_x_
scale_y_

Exercise III

Create a scatter plot with CPI on the x axis and HDI on the y axis. Color the points to indicate region.
Modify the x, y, and color scales so that they have more easily-understood names (e.g., spell out “Human development Index” instead of “HDI”).
Modify the color scale to use specific values of your choosing. Hint: see ?scale_color_manual.

ggplot(dat, aes(x=CPI, y=HDI)) +
  theme(text=element_text(size=9)) +
  geom_point(aes(color=Region)) +
  scale_x_continuous(name="Corruption Perception Index") +
  scale_y_continuous(name="Human Development Index") +
  scale_color_manual(name="Region of World", 
                     values=c("#24576D",
                              "#099DD7",
                              "#28AADC",
                              "#248E84",
                              "#F2583F",
                              "#96503F"))

Facet

Faceting

# Without faceting, all the lines are crowding together and it's hard to distinguish by state.
ggplot(housing, aes(x=Date, y=Home.Value)) +
  theme(text = element_text(size=8)) +
  geom_line(aes(color=State))

# With faceting, all the 
ggplot(housing, aes(x=Date, y=Home.Value)) +
  theme(text = element_text(size=8)) +
  geom_line() +
  facet_wrap(~State, ncol=10)

Other Examples

library(tidyr)
housing.byyear <- aggregate(cbind(Home.Value, Land.Value) ~ Date, data=housing, mean)
home.land.byyear <- gather(housing.byyear, value = "value", key = "type", Home.Value, Land.Value)

ggplot(home.land.byyear, aes(x=Date, y=value, color=type)) +
  geom_line()

Graph Simulation

library(ggthemes)
library(ggrepel)

dat <- read.csv("./dataSets/EconomistData.csv")
pointsToLabel <- c("Russia", "Venezuela", "Iraq", "Myanmar", "Sudan",
                   "Afghanistan", "Congo", "Greece", "Argentina", "Brazil",
                   "India", "Italy", "China", "South Africa", "Spane",
                   "Botswana", "Cape Verde", "Bhutan", "Rwanda", "France",
                   "United States", "Germany", "Britain", "Barbados", "Norway", "Japan",
                   "New Zealand", "Singapore")

dat$Region <- factor(dat$Region,
                     levels = c("EU W. Europe",
                                "Americas",
                                "Asia Pacific",
                                "East EU Cemt Asia",
                                "MENA",
                                "SSA"),
                     labels = c("OECD",
                                "Americas",
                                "Asia &\nOceania",
                                "Central &\nEastern Europe",
                                "Middle East &\nnorth Africa",
                                "Sub-Saharan\nAfrica"))

ggplot(dat, aes(x=CPI, y=HDI)) +
  theme(legend.position = "top", text=element_text(size=8)) +
  geom_smooth(mapping=aes(linetype='R2'),
              method='lm', formula=y~x+log(x), se=FALSE, 
              color='#DA3C2A', size=0.7) + 
  geom_point(aes(color=Region), shape=21, size=2, stroke=1.5) +
  # labelling points
  geom_text_repel(aes(label=Country),
                  color='grey20',
                  data=subset(dat, Country %in% pointsToLabel),
                  size=2.5,
                  force=10) +
  
  scale_x_continuous(name="Human Development Index, 2011 (1=best)", limits = c(0.9,10.5), breaks = 1:10) +
  scale_y_continuous(name="Corruption Perceptions Index, 2011 (10=least corrupt)", limits = c(0.2,1.0), breaks = seq(0.1, 1, by=0.1)) + 
  scale_color_manual(name="", values = c("#24576D","#099DD7","#28AADC","#248E84","#F2583F","#96503F"), guide=guide_legend(nrow = 1)) +
  ggtitle("Corruption and Human Development")

  # + theme_bw()

Graph Simulation - Solution

mR2 <- summary(lm(HDI ~ CPI + log(CPI), data = dat))$r.squared
mR2 <- paste0(format(mR2, digits = 2), "%")

library(ggthemes)
library(ggrepel)

ggplot(dat, aes(x=CPI, y=HDI)) +
  geom_smooth(mapping=aes(linetype="r2"), method='lm', formula=y~x+log(x), se=FALSE, color='#DA3C2A', size=0.8) +
  geom_point(aes(color=Region), shape=1, stroke=1.5) +
  geom_text_repel(mapping=aes(label=Country, alpha=labels),
                  data=transform(dat, labels=Country %in% c("Russia", "Venezuela", "Iraq", "Myanmar", "Sudan",
                                                           "Afghanistan", "Congo", "Greece", "Argentina", "Brazil",
                                                           "India", "Italy", "China", "South Africa", "Spane",
                                                           "Botswana", "Cape Verde", "Bhutan", "Rwanda", "France",
                                                           "United States", "Germany", "Britain", "Barbados", "Norway", "Japan",
                                                           "New Zealand", "Singapore")),
                  color='gray40',
                  segment.color='gray80',
                  size=3) +
  scale_alpha_discrete(range=c(0,1), guide=FALSE) +
  scale_x_continuous(name="Corruption Preception Index, 2011 (10=least corrupt)",
                     limits=c(1.0, 10.0),
                     breaks=1:10) +
  scale_y_continuous(name="Human Development Index, 2011 (1=best)",
                     limits=c(0.2, 1.0),
                     breaks=seq(0.2,1,by=0.1)) +
  scale_color_manual(name="",
                     values=c("#24576D","#099DD7","#28AADC","#248E84","#F2583F","#96503F"),
                     guide=guide_legend(nrow=1)) + 
  scale_linetype(name='',
                 breaks='r2',
                 labels=list(bquote(R^2==.(mR2))),
                 guide=guide_legend(override.aes = list(linetype=1, size=2, color="#DA3C2A"))) +
  ggtitle("Corruption and human development") +
  
  theme_bw() +
  theme(panel.border = element_blank(),
        panel.grid = element_blank(),
        panel.grid.major.y = element_line(color='gray'),
        #panel.grid.major.x = element_line(color='gray'),
        
        axis.line.x = element_line(color='gray'),
        axis.text = element_text(face='italic'),
        axis.title.x = element_text(face='italic', size=8, color='gray20'),
        axis.title.y = element_text(face='italic', size=8, color='gray20'),
        
        legend.position = 'top',
        legend.direction = 'horizontal',
        legend.box= 'horizontal',
        legend.text = element_text(size=8,color='gray20'),
        
        plot.title = element_text(size=13, face = 'bold')
        )

http://www.sthda.com/english/wiki/be-awesome-in-ggplot2-a-practical-guide-to-be-highly-effective-r-software-and-data-visualization

Tutorial on ggplot

Nicole Li

3/12/2018

http://tutorials.iq.harvard.edu/R/Rgraphics/Rgraphics.html#exercise_iii

`ggplot`

`ggplot` VS. Basic Plots

Aesthetics and Geometric Objects

Exercise I

Statistical Transformations

Exercise II

Scales

Available Scales Categories:

Exercise III

Facet

Other Examples

Graph Simulation

Graph Simulation - Solution

Tutorial on ggplot

Nicole Li

3/12/2018

http://tutorials.iq.harvard.edu/R/Rgraphics/Rgraphics.html#exercise_iii

ggplot

ggplot VS. Basic Plots

Aesthetics and Geometric Objects

Exercise I

Statistical Transformations

Exercise II

Scales

Available Scales Categories:

Exercise III

Facet

Other Examples

Graph Simulation

Graph Simulation - Solution

`ggplot`

`ggplot` VS. Basic Plots